Jiang Wangmeng, Ma Qiang
Faculty of Humanities and Social Sciences, Beijing University of Technology, Beijing, China.
Faculty of Department of Foreign Language and Tourism, Hebei Petroleum University of Technology, Chengde, Hebei, China.
PeerJ Comput Sci. 2023 Sep 7;9:e1486. doi: 10.7717/peerj-cs.1486. eCollection 2023.
In order to optimize the integration of English multimedia resources and achieve the goal of sharing English teaching resources in education, this article reconstructs the traditional college English curriculum system. It divides professional English into learning modules according to different majors integrating public health teaching resources. How optimize the integration of English multimedia resources and achieving the goal of sharing English teaching resources (ETR) is the main direction of English teaching reform during the current COVID-19 pandemic. An English multimedia teaching resource-sharing platform is designed to extract feature items from multimedia teaching resources using the ID3 information gain method and construct a decision tree for resource push. In resource sharing, a structured peer-to-peer network is used to manage nodes, query location and share multimedia teaching resources. The optimal gateway node is selected by calculating the distance between each gateway node and the fixed node. Finally, a collaborative filtering (CF) algorithm recommends Multimedia ETR to different users. The simulation results show that the platform can improve the sharing speed and utilization rate of teaching resources, with maximum throughput reaching 12 Mb/s and achieve accurate recommendations of ETR.
为了优化英语多媒体资源整合，实现教育领域英语教学资源共享的目标，本文重构了传统大学英语课程体系。它根据不同专业将专业英语划分为学习模块，整合公共卫生教学资源。如何优化英语多媒体资源整合并实现英语教学资源（ETR）共享目标是当前新冠疫情期间英语教学改革的主要方向。设计了一个英语多媒体教学资源共享平台，使用ID3信息增益方法从多媒体教学资源中提取特征项，并构建用于资源推送的决策树。在资源共享中，使用结构化对等网络来管理节点、查询位置并共享多媒体教学资源。通过计算每个网关节点与固定节点之间的距离来选择最优网关节点。最后，采用协同过滤（CF）算法向不同用户推荐多媒体ETR。仿真结果表明，该平台能够提高教学资源的共享速度和利用率，最大吞吐量达到12 Mb/s，并实现ETR的准确推荐。
